An Analysis of Critical Factors in Medical Device Development to Design for FDA

Abstract

This paper provides a deeper understanding on the Food and Drug Administration’s (FDA) role in relation to medical devices. Specific considerations are given to the data FDA reports to the public domain as part of the 510(k) clearances and PMA approvals. Scientific treatment of such considerations has been a void in the literature prior to the work presented here. Critical factors are defined at the product level, where an empirical investigation is performed to study the impact of various factors in FDA’s decision time. The critical factors identified include regulatory components, product characteristics and the historical reference. Significant regulatory components include the submission types and the different types of classifications (product codes, risk classification and regulation number). Significant product characteristics included the factors specific to hip devices (cemented, constrained) and generalized factors applicable to most medical devices (intended use, context of use, function and material). The importance of historical reference, as an indication of various types of experience, showed the significance of company’s experience with FDA, and FDA’s experience in terms of product codes and product characteristics (body part, function, material and et cetera).

Critical success factors of medical technology supply chains

“…Research on Critical Success Factors in the Medical Technology industry in recent years has been focused towards medical device development, as companies in this market face intense competition, higher degrees of customer sophistication, and where the cost of medical devices greatly influences healthcare systems (Medina et al, 2013). Medina et al (2012) distinguishes between internal and external factors influencing medical device development. Internal factors focus on the organisational context within which design is carried out.…”

An investigation of critical factors in medical device development through Bayesian networks

“…In order to address the shortcomings of survey-based studies and the time constraints of following the complete development process, prior research efforts employed statistical methods (e.g., ANCOVA) to explain important factors in relation to FDA's decision time (Medina et al, 2012). However, this analysis did not allow for a holistic study of quantitative variables that were related given that variables with strong correlations should not be included together in the ANCOVA analysis.…”

“…Note that the information contained in the table was a deduction of our review of the literature (presented in Medina et al, 2011;Medina et al, 2012;Medina et al, 2013) as well as an evaluation of the FDA data and how the available data could be used to represent factors cited in the literature. As can be observed in the table, for each variable we have listed if they are internal-or external-variable, and quantitative-or qualitative-7 variable; we have also associated each variable either to the product, to the company or the regulatory environment (FDA).…”

“…Many factors are related to the likelihood of success of devices in the market; and based on a company's ability to react to them, these factors are considered to be either internal or external (Medina et al, 2012). Internal factors mostly focus on the 2 organizational context within which design is executed, and among others these factors include organization's composition in terms of the level of experience in design teams (Lucke et al, 2008) and effective communication of the development priorities (Brown et al, 2008).…”

“…Success factors in product development have been examined in various ways thus far. However, existing methods used for the identification of critical success factors have a number of shortcomings; among these are the subjectivity of survey-based studies and the complexity to comprehensively and rigorously address both internal and external factors (Medina et al, 2012). This paper discusses these shortcomings and proposes to overcome them with the application of a Bayesian network (BN) approach to examine the impact of product characteristics, company context and regulatory environment related factors on MDD.…”